Neural Network Training For Three Dimensional (3D) Gaze Prediction With Calibration Parameters

ABSTRACT

Techniques for generating 3D gaze predictions based on a deep learning system are described. In an example, the deep learning system includes a neural network. The neural network is trained with training images. During the training, calibration parameters are initialized and input to the neural network, and are updated through the training. Accordingly, the network parameters of the neural network are updated based in part on the calibration parameters. Upon completion of the training, the neural network is calibrated for a user. This calibration includes initializing and inputting the calibration parameters along with calibration images showing an eye of the user to the neural network. The calibration includes updating the calibration parameters without changing the network parameters by minimizing the loss function of the neural network based on the calibration images. Upon completion of the calibration, the neural network is used to generate 3D gaze information for the user.

TECHNICAL FIELD

The present application relates to gaze detection systems and methods.In an example, such systems and methods rely on deep learning systems,such as neural networks to detect three dimensional (3D) gaze.

BACKGROUND

Interaction with computing devices is a fundamental action in today'sworld. Computing devices, such as personal computers, tablets,smartphones, are found throughout daily life. In addition, computingdevices that are wearable, such as wearable headset devices (e.g.,virtual reality headsets and augmented reality headsets), are becomingmore popular. The systems and methods for interacting with such devicesdefine how they are used and what they are used for.

Advances in eye tracking technology have made it possible to interactwith a computing device using a person's gaze information. In otherwords, the location on a display the user is gazing at. This informationcan be used for interaction solely, or in combination with acontact-based interaction technique (e.g., using a user input device,such as a keyboard, a mouse, a touch screen, or another input/outputinterface).

Previously proposed interaction techniques using gaze information can befound in U.S. Pat. No. 6,204,828, United States Patent ApplicationPublication 20130169560, U.S. Pat. No. 7,113,170, United States PatentApplication Publication 20140247232, and U.S. Pat. No. 9,619,020. Thefull specification of these patents and applications are hereinincorporated by reference.

Generally, gaze-based interaction techniques rely on detecting a gaze ofa user on a gaze point. Existing systems and methods can accuratelydetect two dimensional (2D) gaze. Recently, neural networks have beenimplemented to detect such 2D gazes.

Attempts have been made to expand existing techniques that rely onneural network to three dimensional (3D) gaze. However, the accuracy ofthe prediction is not as good as the one for 2D gaze. Absent accurate 3Dgaze tracking, support of stereoscopic displays and 3D applications issignificantly limited. Further, even in the 2D domain, a neural networkis typically trained for a specific camera and screen configuration(e.g., image resolution, focal length, distance to a screen of acomputing device, a size of the screen, and the like). Thus, anytime theconfiguration changes (e.g., different mage resolution, different screensize, and the like), the neural network can no longer predict 2D gaze atan acceptable accuracy. Re-training of the neural network for the newconfiguration would be needed.

SUMMARY

Embodiments of the present disclosure relate to three dimensional (3D)gaze detection based on a deep learning system. In an example, acomputer system trains a neural network. The training includes inputtinga training image and a first calibration parameter to the neuralnetwork. The training image shows an eye of a person. The training alsoincludes updating the first calibration parameter and a networkparameter of the neural network based on minimizing a loss function ofthe neural network. The loss function is minimized based on the trainingimage and the first calibration parameter. Upon completion of thetraining, the computer system calibrates the neural network for a user.The calibration includes inputting a calibration image and a secondcalibration parameter to the neural network. the calibration image showsan eye of the user based on image data generated by a camera associatedwith an eye tracking system of the user. The calibration also includesupdating the second calibration parameter without updating the networkparameter of the neural network. The second calibration parameter isupdated by at least minimizing the loss function based on thecalibration image and the second calibration parameter. Upon completionof the calibrating, g by the computer system generates three dimensional(3D) gaze information. Generating the 3D gaze information includesinputting an image and the second calibration parameter to the neuralnetwork The image shows the eye of the user based on additional imagedata generated by the camera. Generating the 3D gaze information alsoincludes receiving a prediction from the neural network based on theimage and the second calibration parameter. The prediction comprises adistance correction, a two dimensional (2D) gaze origin of the eye ofthe user in the image, and a 2D gaze direction of the eye of the user inthe image.

These illustrative embodiments are mentioned not to limit or define thedisclosure, but to provide examples to aid understanding thereof.Additional embodiments are discussed in the Detailed Description, andfurther description is provided there.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of variousembodiments may be realized by reference to the following figures. Inthe appended figures, similar components or features may have the samereference label. Further, various components of the same type may bedistinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

FIG. 1 shows an eye tracking system, according to an embodiment.

FIG. 2 shows an example of an image of an eye captured by an imagesensor, according to an embodiment.

FIG. 3 is a block diagram illustrating a specialized computer system,according to an embodiment.

FIG. 4 shows an example of a wearable computing device that implementscomponents of an eye tracking system, according to an embodiment.

FIG. 5 illustrates an example computing environment for predicting 3Dgaze based on a deep learning system, according to an embodiment.

FIG. 6 illustrates example components of a deep learning system forpredicting 3D gaze, according to an embodiment.

FIG. 7 illustrates an example network architecture for a neural network,according to an embodiment.

FIG. 8 illustrates an example image normalization, according to anembodiment.

FIG. 9 illustrates an example 3D gaze prediction, according to anembodiment.

FIG. 10 illustrates predictions for 2D gaze vectors and distancecorrection generated by a neural network, according to an embodiment.

FIG. 11 illustrates an example flow for predicting 3D gaze based on adeep learning system, according to an embodiment.

FIG. 12 illustrates a strategy for using training images that areassociated with a diversity of gaze point locations relative to 2Dplanes of cameras that generated the training images, according to anembodiment.

FIG. 13 illustrates an example flow for training a neural network,according to an embodiment.

FIG. 14 illustrates an example flow for training a neural network usingembedded calibration parameters, according to an embodiment.

DETAILED DESCRIPTION

Embodiments of the present disclosure relate to three dimensional (3D)gaze detection based on a deep learning system. In an example, a neuralnetwork is used. This neural network is usable independently of a cameraand screen configuration. In other words, regardless of the specificcamera, screen, and combination of camera and screen configuration, theneural network is properly trained to predict gaze information insupport of 3D gaze detection.

More particularly, a camera captures a two dimensional (2D) image of auser gazing at a point in 3D space. A rough distance between the cameraand the user's eyes is estimated from this 2D image. The 2D image isnormalized to generate warped images centered around the user's eye at ahigh resolution and a warped image around the user's face is generatedat a low resolution. These warped images are input to the neural networkthat, in turn, predicts a 2D gaze vector per eye and a distancecorrection for the rough distance. A position of eye in 3D space isestimated based on rough distance and the distance correction, and aposition of the camera in the 3D space. Based on a mapping functionbetween the 2D space and the 3D space, each 2D gaze vector is mapped toa 3D gaze direction. The 3D position of an eye and its 3D gaze directionindicate the 3D gaze associated with that eye.

Because, a normalized 2D image is used and because the neural networkpredicts a distance correction and 2D gaze vectors, the neural network'spredictions do not depend on the camera and screen configuration. Hence,that same trained neural network can be used across different eyetracking systems including ones integrated with different types ofsmartphones, tablets, laptops, wearable headset devices (e.g., virtualreality and augmented reality headsets), and standalone eye trackingsystems. Further, because 3D gaze is detected, stereoscopic displays and3D applications can be supported.

The training of the neural network generally relies on training imagesthat are diverse with respect to the locations of gaze points relativeto 2D planes of cameras used to capture the training images (e.g., foreach camera, an X, Y plane perpendicular to the camera's line-of-sight).In particular, some of the training images show user eyes that gazed atgaze points located in the 2D planes of the cameras, while othertraining images show user eyes that gazed at gaze points outside of the2D planes. During the training, the neural network looks for gaze anglesbetween user eyes-cameras and user uses-gaze points and eye-to-cameradistances. Because diversity is used, the neural network can correctlyfind the angles and the distances. Gaze lines (also referred to as gazerays) are predicted from the gaze angles and distances between the gazepoints and the gaze lines (gaze point-to-gaze line distances) arecomputed. The loss function of the neural network involves gazepoint-to-gaze line distances. During the training, the parameters of theneural network (e.g., weights of connection between nodes at thedifferent layers) are updated to minimize the loss function byminimizing the difference between the true and predicted gazepoint-to-gaze line distances. In the interest of brevity, from hereinforward, an image refers to a 2D image, unless otherwise indicated.

These and other features of training and using the neural network for 3Dgaze prediction independently of a camera and screen configuration arefurther described herein below. Various components and configurations ofeye tracking systems are described herein next to provide a betterunderstanding of the 3D gaze prediction techniques.

Furthermore, during the training, the neural network learns “n”calibration parameters for the persons shown in the training images. Adesigner of the neural network needs only to specify the number “n”rather than actually specifying the calibration parameters. Theseparameters are input to layers of the neural network and become a partof the training. Upon completion of the training, an end user operatinga tracking system typically follows a calibration process. Part of thisprocess, calibration images (e.g., where the end user is asked to gazeat particular points) are input to the neural network that thengenerates the “n” calibration parameters for the user. These parametersare used in the gaze prediction for the end user.

FIG. 1 shows an eye tracking system 100 (which may also be referred toas a gaze tracking system), according to an embodiment. The system 100comprises illuminators 111 and 112 for illuminating the eyes of a user,and an image sensor 113 for capturing images of the eyes of the user.The illuminators 111 and 112 may for example, be light emitting diodesemitting light in the infrared frequency band, or in the near infraredfrequency band. The image sensor 113 may for example be a camera, suchas a complementary metal oxide semiconductor (CMOS) camera or a chargedcoupled device (CCD) camera. The camera is not limited to be an IRcamera or a depth camera or a light-field camera. The shutter mechanismof the image sensor can either be a rolling shutter or a global shutter.

A first illuminator 111 is arranged coaxially with (or close to) theimage sensor 113 so that the image sensor 113 may capture bright pupilimages of the user's eyes. Due to the coaxial arrangement of the firstilluminator 111 and the image sensor 113, light reflected from theretina of an eye returns back out through the pupil towards the imagesensor 113, so that the pupil appears brighter than the iris surroundingit in images where the first illuminator 111 illuminates the eye. Asecond illuminator 112 is arranged non-coaxially with (or further awayfrom) the image sensor 113 for capturing dark pupil images. Due to thenon-coaxial arrangement of the second illuminator 112 and the imagesensor 113, light reflected from the retina of an eye does not reach theimage sensor 113 and the pupil appears darker than the iris surroundingit in images where the second illuminator 112 illuminates the eye. Theilluminators 111 and 112 may for example, take turns to illuminate theeye, so that every first image is a bright pupil image, and every secondimage is a dark pupil image.

The eye tracking system 100 also comprises circuitry 120 (for exampleincluding one or more processors) for processing the images captured bythe image sensor 113. The circuitry 120 may for example, be connected tothe image sensor 113 and the illuminators 111 and 112 via a wired or awireless connection. In another example, circuitry 120 in the form ofone or more processors may be provided in one or more stacked layersbelow the light sensitive surface of the image sensor 113.

FIG. 2 shows an example of an image of an eye 200, captured by the imagesensor 113. The circuitry 120 may for example, employ image processing(such as digital image processing) for extracting features in the image.The circuitry 120 may for example employ pupil center cornea reflection(PCCR) eye tracking to determine where the eye 200 is looking. In PCCReye tracking, the processor 120 estimates the position of the center ofthe pupil 210 and the position of the center of a glint 220 at the eye200. The glint 220 is caused by reflection of light from one of theilluminators 111 and 112. The processor 120 calculates where the user isin space using the glint 220 and where the user's eye 200 is pointingusing the pupil 210. Since there is typically an offset between theoptical center of the eye 200 and the fovea, the processor 120 performscalibration of the fovea offset to be able to determine where the useris looking. The gaze directions obtained from the left eye and from theright eye may then be combined to form a combined estimated gazedirection (or viewing direction). As will be described below, manydifferent factors may affect how the gaze directions for the left andright eyes should be weighted relative to each other when forming thiscombination.

In the embodiment described with reference to FIG. 1, the illuminators111 and 112 are arranged in an eye tracking module 110 placed below adisplay watched by the user. This arrangement serves only as an example.It will be appreciated that more or less any number of illuminators andimage sensors may be employed for eye tracking, and that suchilluminators and image sensors may be distributed in many different waysrelative to displays watched by the user. It will be appreciated thatthe eye tracking scheme described in the present disclosure may, forexample, be employed for remote eye tracking (for example in a personalcomputer, a smart phone, or integrated in a vehicle) or for wearable eyetracking (such as in virtual reality glasses or augmented realityglasses).

FIG. 3 is a block diagram illustrating a specialized computer system 300in which embodiments of the present disclosure may be implemented. Thisexample illustrates a specialized computer system 300 such as may beused, in whole, in part, or with various modifications, to provide thefunctions of components described herein.

Specialized computer system 300 is shown comprising hardware elementsthat may be electrically coupled via a bus 390. The hardware elementsmay include one or more central processing units 310, one or more inputdevices 320 (e.g., a mouse, a keyboard, eye tracking device, etc.), andone or more output devices 330 (e.g., a display device, a printer,etc.). Specialized computer system 300 may also include one or morestorage devices 340. By way of example, storage device(s) 340 may bedisk drives, optical storage devices, solid-state storage devices suchas a random access memory (“RAM”) and/or a read-only memory (“ROM”),which can be programmable, flash-updateable and/or the like.

Specialized computer system 300 may additionally include acomputer-readable storage media reader 350, a communications system 360(e.g., a modem, a network card (wireless or wired), an infra-redcommunication device, Bluetooth™ device, cellular communication device,etc.), and working memory 380, which may include RAM and ROM devices asdescribed above. In some embodiments, specialized computer system 300may also include a processing acceleration unit 370, which can include adigital signal processor, a special-purpose processor and/or the like.

FIG. 4 shows an example of a wearable computing device 400 thatimplements some or all of the above components of an eye tracking systemas described in connection with FIGS. 1-2. The wearable computing device400 can be a VR headset or an AR headset that can be worn by a user. Asillustrated, the wearable computing device 400 includes a set of lenses410, such as Fresnel lenses, a set of cameras 420, a set of hot mirrors430 (e.g., as further illustrated in FIGS. 12-14, the set includes twohot mirrors for each eye in various embodiments), and a set of displays440. The camera 420 can include the image sensors 113 of FIG. 1.Although not shown in FIG. 4, the wearable computing device 400 can alsoinclude a set of illuminators and processing circuitry. These and othercomponents can be integrated within a housing 450 of the wearablecomputing device 400. In this way, upon the user mounting the wearablecomputing device 400 on his or her head, the set of lenses 410 would berelatively close to the user's eyes and the set of displays would berelatively far from the user's eye, and the remaining components may belocated in between. The arrangement of these components allows thedetection of the user's gaze point in three dimensional virtual or realspace.

Herein next, the use of a deep learning system for 3D gaze prediction isdescribed. In the interest of clarity of explanation, this system isdescribed in connection with a camera, a screen, and two user eyes(e.g., the camera captures images, some or all of which show the twouser eyes). The deep learning system can be used with an arbitrarycamera and screen configuration for eye tracking that uses visiblelight, passive infrared, active bright-pupil (BP) infrared, and thelike. However, the embodiments of the present disclosure are not limitedas such.

For example, the embodiments similarly apply to an eye tracking systemthat uses one camera per user eye, such as in the context of a virtualreality headset or an augmented reality headset. Changes to how the deeplearning system is implemented for a one camera per eye tracking shouldbe apparent to one skilled in the art in light of the presentdisclosure. For example, rather than inputting two warped images, eachfocusing on one of the user eyes, only a single warped image of the usereye that is associated with the camera is used. Furthermore, no warpedimage around the user face is may be input. During the training, theneural network learns to predict a distance correction from the warpedimage rather that predicting this correction based on warped imagesaround the user eyes and around the user face. This system would thenoutput a 2D gaze vector for the user eye associated with the camera anda distance correction for the rough camera-to-eye distance. In addition,in the case of a VR or AR device, a rough distance may be predefined andneed not be estimated based on an image generated by the camera.

FIG. 5 illustrates an example computing environment for predicting 3Dgaze based on a deep learning system, according to an embodiment.Generally, 2D gaze information refers to an X, Y gaze position on a 2Dplane. In comparison, 3D refers to not only the X, Y gaze position, butalso the Z gaze position. In an example, the 3D gaze can becharacterized by an eye position in 3D space as the origin and adirection of the 3D gaze from the origin.

As illustrated in FIG. 5, a user 510 operates a computing device 520that tracks the 3D gaze 512 of the user 510. To do so, the computingdevice 520 is, in an example, in communication with a server computer530 that hosts a deep learning system 532. The computing device 520sends, to the server computer 530 over a data network (not shown), a 2Dimage 550 showing the user eyes while the user 510 is gazing. The servercomputer 530 inputs this 2D image 550 to the deep learning system 532that, in response, predicts the 3D gaze 512. The server computer 530sends information 560 about the 3D gaze 512, such as the 3D eye positionand 3D gaze direction back to the computing device 520 over the datanetwork. The computing device 520 uses this information 560 to provide a3D gaze-based computing service to the user 510.

Although FIG. 5 shows the computer server 530 hosting the deep learningsystem 532, the embodiments of the present disclosure are not limited assuch. For example, the computing device 530 can download code and hostan instance of the deep learning system 532. In this way, the computingdevice 520 relies on this instance to locally predict the 3D gaze 512and need not send the 2D image 550 to the server computer 530. In thisexample, the server computer 530 (or some other computer systemconnected thereto over a data network) can train the deep learningsystem 532 and provide an interface (e.g., a web interface) fordownloading the code of this deep learning system 530 to computingdevices, thereby hosting instances of the deep learning system 530 onthese computing devices.

In an example, the computing device 520 includes a camera 522, a screen524, and a 3D gaze application 526. The camera 522 generates the 2Dimage 550 that is a 2D representation 540 of the user's face. This 2Dimage 550 shows the user eyes while gazing in 3D space. A 3D coordinatesystem 528 can be defined in association with the camera 522. Forexample, the camera 522 is at the origin of this 3D coordinate system528. The X and Y planes can be a plane perpendicular to the camera's 522line-of-sight. In comparison, the 2D image 550 has a 2D plane that canbe defined around a 2D coordinate system 542 local to the 2Drepresentation 540 of the user's face. The camera 522 is associated witha mapping between the 2D space and the 3D space (e.g., between the twocoordinate systems 542 and 528). In an example, this mapping includesthe camera's 522 back-projection matrix and is stored locally at thecomputing device 522 (e.g., in storage location associated with the 3Dgaze application 526).

The screen 524 may, but need not be, in the X, Y plane of the camera 522(if not, the relative positions between the two is determined based onthe configuration of the computing device 520). The 3D gaze application526 can process the 2D image 550 for inputting to the deep learningsystem 530 (whether remote or local to the computing device 520) and canprocess the information 560 about the 3D gaze to support stereoscopicdisplays (if also supported by the screen 524) and 3D applications(e.g., 3D controls and manipulations of displayed objects on the screen524 based on the information 560).

FIG. 6 illustrates example components of a deep learning system 600 forpredicting 3D gaze, according to an embodiment. As illustrated, the deeplearning system includes an eye detector 620, a distance estimator 630,an image generator 640, a neural network 650, and a 3D gaze generator660. Some or all these components can be implemented as specializedhardware and/or as software modules (e.g., specific computer-readableinstructions) hosted on specialized or general processing hardware.

As illustrated, a 2D image 610 is input to the eye detector 620. Forexample, this 2D image 610 is generated with a camera. In response, theeye detector 620 detects the user eyes in the 2D image 610 and outputsinformation about the positions 622 of the eyes in the image (e.g.,locations of these centers of the pupils in the 2D plane of the 2D image610). In an example, the eye detector 620 is implemented as a machinelearning algorithm trained for eye detection. Many machine learningalgorithms are possible and are known to one skilled in the art.

The eye positions 622 and the 2D image 610 are input to the distanceestimator 630. In response, the distance estimator 630 generates anestimated distance 632, such as a rough distance. To do so, the distanceestimator 630 projects the eyes detected in the 2D image 610 into a 3Dcoordinate system centered around the camera. This projection uses the2D-3D space mapping of the camera. The distance estimator 630 searchesfor the eye projections in the 3D space where interocular distance (ID)is about the average human ID (e.g., sixty-three millimeters). Thedistance between the camera and each of these eye projections can bereferred to as a projected distance. The rough distance is set as afunction of the average human ID and the projected distances.

To illustrate, let d_(rough) refer to the rough distance and K to theintrinsic camera matrix, and e_(left) and e_(right) the detected eyes inthe 2D image 610. K is a component of the camera's 2D-3D space mapping.The projected left and right eyes e′_(left,proj) and e′_(right,proj) arecomputed as e′_(left,proj)=K⁻¹e_(left) and e′_(right,proj)=K⁻¹e_(right)and represent eye vectors projected from detected eyes in the 2D image610 into the 3D coordinate system centered around the camera, as shownin FIG. 8. These projected eyes are normalized ase_(left,proj)=e′_(left,proj)=e′_(left,proj)/∥e′_(left,proj)∥ ande_(right,proj)=e′_(right,proj)/∥e′_(right,proj)∥. The rough distance iscomputer as d_(rough)=ID/μe_(left,proj)−e_(right,proj)∥.

The image generator 640 receives the 2D image 610 and, in response,generates warped images 642 around the user eyes and a warped image 644around the user face. In an example, the image generator 640 accessesfrom local memory a first predefined distance s in pixels for the usereye warped images 642 and a second predefined distance s in pixels forthe user face warped image 644. These predefined distances s aredifferent interocular distances in pixels s, such that the user eyewarped images 642 and the user face warped image 644 are at differentprojections in the 3D space and have different resolution (theresolution of the user eye warped images 642 being higher than the oneof the user face warped image 644 by using a smaller first predefineddistance s relative to the second predefined distance s). The imagegenerator 640 uses these predefined distances s to generate the warpedimages such that they are normalized relative to the camera. Inparticular, each warped image represents a projection of the 2D image610 in the 3D coordinate system centered around the camera, a rotationof the 2D image 610 around the X-axis (such that any head tilt isrotated to be in the horizontal position), a scaling of the 2D image 610(based on the predefined distances s), and a warping (such that the usereyes and the user face are at the center of each warped image and arenot geometrically skewed). In other words, each warped image representsa normalized image that can be input to the neural network 650, wherethe normalization decouples the dependency of this input to the cameraconfiguration (e.g., image resolution, focal length, distance to ascreen, camera type such as pinhole and non-pinhole, and the like).

To illustrate, the image generator 640 generates a rotation matrix Rthat rotates points from the real 3D space (e.g., the 3D coordinatesystem centered around the camera) to a normalized 3D space (e.g., a 3Dcoordinate system also centered around the camera but rotated relativeto the 3D real space such that the vector between the user eyes in the2D image 610 is horizontal), as further shown in FIG. 8. The imagegenerator 640 also generates a scaling matrix M based on a predefineddistance s (a matrix M is generated for the user eyes and another matrixM is generated for the user face). For example, M is generated as adiagonal matrix, where M=dig([1,1, f]), wherefis a focal length selectedto make the interocular distance between the users eyes to be equal tothe predefined distance s. A transformation T is defined as a functionof the intrinsic matrix K, the rotation matrix R, and the scaling matrixM and is used to normalize the 2D image 610 into a normalized image (forthe user eye and for the user face depending on the M matrix). Forexample, the transformation T is expressed as T=MRK⁻¹ and is applied tothe 2D image 610 to generate a first normalized image at the firstpredefined distance s for the user eyes and a second normalized image atthe second predefined distance s for the user face. Each of thesenormalized images is a rotation and a projection of the 2D image 610from the real 3D space to the normalized 3D space, where the rotated andprojected image is at the predefined distance s in the normalized 3Dspace. The image generator 640 generates a user eye warped image 642 bywarping the normalized user eye image using bilinear interpolation andcrop out a W×H region centered around one of the user eyes. Similarly,the image generator 640 generates the user face warped image 644 bywarping the normalized user face image using bilinear interpolation andcrop out a W×H region centered around the middle point between the usereyes.

Furthermore, one of the eye warped images 642 is mirrored such that theresulting mirrored image aligns with the other (un-mirrored) eye warpedimage. For example, the warped image around the left eye is mirrored. Asa result, the inner canthus of the left eye aligns with the innercanthus of the right eye, and the outer canthus of the left eye alignswith the outer canthus of the right eye, as shown in the mirrored lefteye image and warped right eye image in FIG. 10. By having thisalignment, the eye warped image input to the neural network is the samein terms of orientation, thereby simplifying the architecture and thetraining of the neural network.

The eye warped images 642 and the face warped image 644 are input to theneural network 650. In an example, the 2D image 610 is not input to thisnetwork 650. In response, the neural network 650 outputs a distancecorrection 652 and 2D gaze vectors 654 (one per eye). The distancecorrection 652 is a multiplicative correction factor that can multipliedwith the estimated distance 632 to correct this estimate and generate acorrected distance. Each of the 2D gaze vectors 654 has a gaze origin(e.g., the center of the pupil or a glint) and a gaze direction in thecorresponding eye cropped image (the origin and direction are in the 2Dplane of this image and can be traced back to the real 3D space).

In an example, the neural network 650 is a convolutional neural networkthat includes multiple subnetworks (e.g., along parallel branches of theneural network 650). These subnetworks (and, equivalently, the wholeconvolutional neural network) can be trained in conjunction. Example ofa network architecture is illustrated in FIG. 7. Examples of thetraining are further described in connection with FIGS. 12 and 13. Eachof the warped eye images 642 is input to a subnetwork. These images 642can be input in conjunction (e.g., the two images as parallel inputs) orseparately from each other (e.g., one image input at a time, where thefirst subnetwork would predict the gaze direction from that image). Inresponse, the subnetwork generates a 2D gaze vectors 654 per eye (e.g.,corresponding to the user eye shown in the input image). Each 2D gazevector can be expressed as a 2D gaze origin 02D (e.g., the user eye 2Dlocation in the image plane) and a 2D gaze direction d_(2D). The facewarped image 644 is also input to a subnetwork that, in response, thedistance correction c 652. Hence, the output from the neural networkincludes five components: a 2D gaze origin o_(2D) and a 2D gazedirection d_(2D) per eye and a distance correct c.

The distance correction 652, the estimated distance 632, and the 2D gazevectors 654 are input to the 3D gaze generator 660. In response, the 3Dgaze generator 660 generates and outputs a 3D gaze 662. In an example,the 3D gaze 662 includes a 3D gaze direction per user eye (which can beexpressed in the 3D real space) and a 3D position of the user eye (whichcan also be expressed in the 3D real space).

To illustrate, a corrected distance d_(corr) is generated asd_(corr)=d_(rough)×c. Referring to one of the eyes and its 2D gazevector, the 3D gaze generator 660 computes its 3D gaze based on thecorrected distance and the 2D to 3D space mapping (e.g., the relevantmatrices). For instance, the 3D gaze origin o_(3D,N) in the normalized3D space is computed as o′_(3D,N)=M⁻¹o_(2D) ando_(3D,N)=o′_(3D,N)/∥o′_(3D,N)∥d_(corr). To generate the 3D gazedirection in the normalized 3D space, a normalized basis vector (X,Y,Z)is generated first, where Z′=M⁻¹o_(2D), Y′=[0,1,0]^(T)×Z′, and X′=Y′×Z′and X=X′/∥X′∥, Y=Y′/∥Y′∥, and Z=Z′/∥Z′∥. A normalized 3D gaze directiond_(3D,N) is generated as d′_(3D,N)=[X,Y]d_(2D)−Z andd_(3D,N)=d′_(3D,N)/∥d′_(3D,N)/∥d′_(3D,N)∥. The 3D gaze original and the3D gaze direction are mapped from the normalized 3D space to the real 3Dspace based on the rotation matrix R. For instance, the 3D gaze origino_(3D) in the real 3D space is computed as o_(3D)=R⁻¹o_(3D,N).Similarly, the 3D gaze direction d_(3D) in the real 3D space is computedas d_(3D)=R⁻¹d_(3D,N).

FIG. 7 illustrates an example network architecture for a neural network,according to an embodiment. As illustrated in FIG. 7, three images710-714 are input to the network: a face warped image 710 at a lowresolution, a left eye warped image 712 at a high resolution, and aright eye warped image 714 at the high resolution (where, “low” and“high” are relative term, such as “low resolution” refers to being lowerthan the “high resolution”). The eye warped images 712 and 714 arecentered on the eye detections, x=e_(left or right) scaled to aninterocular distance of s=320 pixels and cropped to 224×112 pixels. Theright eye image 714 is mirrored 716 by modifying the rotation matrix R.This provides the network with a consistent appearance. The face warpedimage 710 is centered on the midpoint between the eye detections, scaledto =84 pixels and cropped to 224×56 pixels.

The network includes separate convolutional neural networks (CNNs)720-726 for the eyes (shown as CNN 722 for the left eye and CNN 724 forthe right eye, with tied weights) and the face (shown as CNN 720). Bothare the convolutional part of ResNet-18, similarly to what is describedin K. He, X. Zhang, S. Ren, and J. Sun. “Deep residual learning forimage recognition,” CoRR, abs/1512.03385, 2015, the content of which isincorporated herein by reference. The output from all CNNs, 720-726 botheyes and the face, are concatenated through as a concatenation layer 730and fed to a fully connected module 740, which predicts a distancecorrection c.

The output from each eye CNN 722 and 724 is concatenated with a set of“n” personal calibration parameters and the distance correction c. Theconcatenation for the left eye and the right eye is through aconcatenation layer 732 and a concatenation layer 734, respectively. Thecombined feature vector resulting from each concatenation is fed to afully connected module (shown as a fully connected module 742 for theleft eye and a fully connected module 744 for the right eye).

The fully connected modules 720-724 can be described as:FC(3072)-BN-ReLU-DO(0.5)-FC(3072)-BN-ReLU-DO(0.5)-FC({4, 1}). The finaloutput is either the 2D gaze origin and 2D gaze direction, or thedistance correction c.

The “n” calibration parameters need not be provided to the distanceestimation component (e.g., the concatenation layer 739), as it istypically challenging to detect distance errors from calibration datacollected at one distance, which is what is typically available.

The network is trained to minimize the mean minimum miss-distancebetween the predicted gaze lines and the ground-truth stimulus points asillustrated in the next figures. Gaze origins that are outside the eyeimages and distance correction over forty percent in either directionare also penalized.

FIG. 8 illustrates an example image normalization, according to anembodiment. As illustrated, a real 3D space 810 is centered around acamera that generates a real image 830. The 3D space 810 can be definedas a 3D coordinate system. A normalized 3D space 820 is also centeredaround the camera and is generated from the real 3D space 810 based on arotation matrix R. For example, the normalized 3D space 820 correspondsto a rotation of the real 3D space 810 along one of the axis (e.g., theY axis) of the real 3D space 810. The centers between the user eyes forma line. That line is parallel to an axis in the normalized 3D space 820(e.g., the X axis). A normalized image 840 (shown as corresponding to aleft eye warped image) is projected from the real image 830 in thenormalized 3D space (shown as being at a distance away from and parallelto the real image 830). In the case where this normalized image 840corresponds to location where the interocular distance between the twoeyes is the average human ID of 63 mm, the distance between the cameraand the center of the eye in the normalized image 840 is the roughdistance.

FIG. 9 illustrates an example 3D gaze prediction, according to anembodiment. As illustrated, the normalized 3D space 820 of FIG. 8 isused, although the prediction can be mapped to the real 3D space 810 byusing the inverse of the rotation matrix R as described in connectionwith FIG. 6. A 3D gaze origin 910 is derived from a 2D gaze origin 920based on the inverse of a scaling matrix M and a corrected distance 930.The 2D gaze origin 920 and a 2D gaze direction 940 represent a 2D gazevector in the plane of the normalized image 840. A 3D gaze direction 950is derived from the 2D gaze origin 920 and the 2D gaze direction 940based on the inverse of the scaling matrix M as described in connectionwith FIG. 6.

During training, a 3D gaze line (or 3D gaze ray) is projected from the3D gaze origin 910 along the 3D gaze direction 950. A stimulus point 960for the gaze is known (e.g., a known gaze point). The distance 970(e.g., the shortest distance) between the stimulus point 96 and the 3Dgaze line is a distance parameter of the loss function. The lossfunction is minimized by minimizing this distance 970 (e.g., if theneural network properly predicts the 3D gaze, the distance 970 would bezero and the stimulus point 960 would fall on the 3D gaze line).

FIG. 10 illustrates predictions for 2D gaze vectors and distancecorrection generated by a neural network, according to an embodiment.FIG. 10 shows three images 1010, 1020, 1030 that were input to theneural network. Images 1010 and 1020 are eye warped images, whereasimage 1030 is a face warped image. Furthermore, image 1020 (e.g., of theleft eye) is mirrored such that the two images 1010 and 1020 are aligned(e.g., the inner canthus of the left eye aligns with the inner canthusof the right eye, and the outer canthus of the left eye aligns with theouter canthus of the right eye).

The predicted 2D gaze vectors are overlaid over the images 1010 and1020. As shown in the overlay of the image 1010 (and, similarly, in theoverlay of the image 1020), the 2D gaze vector has a gaze origin 1012(e.g., a glint on the right eye) and a gaze direction 1014. Theestimated and corrected distances 1032 are overlaid in the image 1030.As shown, the estimated distance is six-hundred fifty-three millimetersand the corrected distance is four-hundred seventy-five millimeters,representing a predicted distance correction of about seventy-twopercent.

FIG. 11 illustrates an example flow for predicting 3D gaze based on adeep learning system such as the deep learning system 600, in accordancewith an embodiment. An eye tracking system is described as performingthe operations of the example flow. In an example, the eye trackingsystem hosts the deep learning system. In another example, a remotecomputer system hosts the deep learning system and the eye trackingsystem interacts with this remote computer system over a data network toprovide a 2D image and receive a 3D gaze prediction. In yet anotherexample, the deep learning system is distributed between the eyetracking system and the remote computer system (e.g., the remotecomputer system may host the neural network while the eye trackingsystem may host the remaining component of the deep learning system).

Instructions for performing the operations of the illustrative flow canbe stored as computer-readable instructions on a non-transitorycomputer-readable medium of the eye tracking system. As stored, theinstructions represent programmable modules that include code or dataexecutable by a processor(s) of the eye tracking system. The executionof such instructions configures the eye tracking system to perform thespecific operations shown in the figure and described herein. Eachprogrammable module in combination with the processor represents a meansfor performing a respective operation(s). While the operations areillustrated in a particular order, it should be understood that noparticular order is necessary and that one or more operations may beomitted, skipped, and/or reordered.

The example flow starts at operation 1102, where the eye tracking systemreceives an image (e.g., a 2D image). In an example, the image isgenerated by a camera associated with the eye tracking system (e.g., acamera integrated or interfacing with such a system). The image shows auser eye (e.g., when the camera is associated with two user eyes, theimage shows both eyes; when the camera is associated with one user eyeas in the case of a VR headset, the image shows only that user eye).

At operation 1104, the eye tracking system generates a warped around theuser eye from the image. For example, the eye tracking system detectsthe user eye in the image and estimates a rough distance between thecamera and the user eye as detected in the image. The eye trackingsystem also projects the image in a 3D space based on an intrinsicmatrix K of the camera, rotates the projected image based on a rotationmatrix R, and scales the rotated image based on a scaling matrix M. Thescaling matrix M is generated based on predefined distance s in pixelsand the average human interocular distance. The eye tracking system thenwarps the scaled image using bilinear interpretation crops the warpedimage around the user eye based on a predefined region (e.g., one havingpredefined width and height). This image has a first image resolutionthat depends on the predefined distance s.

At operation 1106, the eye tracking system generates a second warpedimage around a second user eye from the image. This operation isperformed if the image shows the second user eye and is, otherwise,skipped. Operation 1106 is similar to operation 1104. The second warpedimage shows the second user eye and has the first image resolution.

At operation 1108, the eye tracking system generates a third warpedimage around the user from the image. This operation is also performedif the image shows the second user eye and is, otherwise, skipped.Operation 1106 is similar to operation 1104, where the scaling is basedon a second predefined distance s (and, thus, a second scaling matrix M)to achieved a second image resolution. The third warped image shows theuser face or at least both user eyes and has the second image resolutionlower than the first image resolution.

At operation 1110, the eye tracking system inputs to a neural networkthe warped image of the user eye. If the other two warped images (e.g.,of the second user eye and the user face) are generated, they are alsoinput to the neural network. The neural network is already trained andpredicts (i) a distance correction c and (ii) a 2D gaze origin and a 2Dgaze direction per eye in the associated warped eye image. Generally,the distance correction c is used to correct the estimated roughdistance and is predicted based on the warped image and, if available,the second and third warped images. The 2D gaze origin and the 2D gazedirection for the user eye (and, similarly, for the second user eye) isgenerated from the warped image (and, similarly, from the second warpedimage) separately from the second and third warped images (and,similarly, separately from the warped and third warped images).

In an example, the neural network is also trained based on “n”calibration parameters that become embedded in the neural network.During a calibration of the eye tracking system, this system generates aplurality of calibration images by instructing the user to gaze at knowngaze points. The calibration images are also normalized and warped andare input to the neural network. The “n” calibration parameters areadjusted such that the loss function of the neural network is minimized.This minimization uses the known gaze points as ground truth. Once theimage is received by the eye tracking system at operation 1102 andprocessed through operations 1106-110, the prediction of the neuralnetwork at operation 1110 uses the “n” calibration parameters.

At operation 1112, the eye tracking system receives from the neuralnetwork the distance correction c. The eye tracking system also receivesfrom the neural network the 2D gaze origin and 2D gaze direction for theuser eye and, as applicable, the 2D gaze origin and 2D gaze directionfor the second user eye.

At operation 1114, the eye tracking system generates a correcteddistance between the user eye and the camera by at least updating theestimated rough distance based on the distance correction c. Forexample, the distance correction c is a correction factor and the eyetracking system multiplies the estimated rough distance by this factorto generate the corrected distance.

At operation 1116, the eye tracking system generates 3D gaze informationfor the user eye from the corrected distance and its 2D gaze origin and2D gaze direction. The 3D gaze information includes a position in the 3Dspace of the user eye (e.g., 3D gaze origin) and a 3D gaze directionoriginating from the user eye. In an example, the 3D gaze origin and 3Dgaze direction are derived from the corrected distance, the 2D gazeorigin, and the 2D gaze direction based on the scaling matrix M and therotation matrix R (or their inverse). This operation can also berepeated to generate a 3D gaze origin and a 3D gaze direction for thesecond user eye based on the corrected distance and its 2D gaze originand 2D gaze direction. In this case, the 3D gaze information includesthe 3D gaze origins and 3D gaze directions of the two user eyes.

At operation 1120, the eye tracking system provides the 3D gazeinformation generated for the user eye and, as applicable, the 3D gazeinformation generated for the second user eye to a 3D gaze application.The 3D gaze application uses the 3D gaze information to supportstereoscopic displays and 3D applications (e.g., 3D controls andmanipulations of displayed objects on a screen).

A neural network of a deep learning system is trained based on trainingimages to predict a distance correction and a 2D gaze vector (e.g., 2Dgaze origin and 2D gaze direction) per user eye. Generally, the trainingis iterative across the training images to minimize a loss function and,accordingly, update the parameters of the neural network throughback-propagation (e.g., one that uses gradient descent). Because theneural network should predict two outputs (the distance correction andthe 2D gaze vector), relying on training images showing user eyes whilegazing at gaze points within 2D planes of the associated cameras thatcaptured these images is insufficient. Instead, diversity of thelocations of gaze points relative to the 2D planes is needed for propertraining. This issue in the training and solution are further describedin connection with FIG. 12.

FIG. 12 illustrates a strategy for using training images that areassociated with a diversity of gaze point locations relative to 2Dplanes of cameras that generated the training images, according to anembodiment. In the interest of clarity of explanation, a single user eyeis referred to in connection with FIG. 12. However, training images thatshow two user eyes are equally applicable. The user eyes captured in thetraining images are also referred to herein as “shown user eyes.” Thecameras used to generate the training images are also referred to hereinas “training cameras.” As illustrated, a camera 1210 generates trainingimages that show a user eye. Each training image is a 2D image capturedwhile the user eye was gazing at a gaze point.

For example, a first training image shows the user while gazing at agaze point 1220. If that first training input was an input to the neuralnetwork during its training, the neural network may incorrectly find thegaze direction of the user eye to the gaze point 1220 and the distanceof the user eye to the camera 1210. As illustrated in FIG. 12, there maybe multiple solutions for the gaze direction and distance (FIG. 12 showstwo of these solutions). In a first solution, the neural network canpredict that the user eye (shown as element 1230 in this solution) isgazing at a first angle “a1” 1232 relative to the gaze point 1220 and isat a first distance “d1” 1234 to the camera 1210. In a second solution,the neural network can predict that the user eye (shown as element 1240in this solution) is gazing at a second angle “a2” 1242 relative to thegaze point 1220 and is at a second distance “d2” 1244 to the camera1210. Both solutions are possible, but only one is correct. Hence, theneural network can predict the incorrect solution, thereby the trainingwould not be proper and would result in a trained neural network thatgenerated inaccurate predictions.

To avoid this potential, a strategy for gaze point location diversity isused. Under this strategy, a second training image of the user eye isadditionally used. In this second training image, either the gaze angleor the user eye-to-camera distance changes. Accordingly, when bothtraining images are used, the set of possible solutions is reduced to asingle solution (e.g., either the first solution or the second solutionin the above example).

Different ways are possible to achieve this diversity. Generally, thetraining images can include two sets of training images. The first setis for training images of user eyes gazing at gaze points within the 2Dplanes of the cameras. The second set is for training images of usereyes gazing at gaze points outside of the 2D planes of the cameras. Inthis way, when the various training images are input to the neuralnetwork, the neural network learns to find the correct solutions.

FIG. 12 illustrates one specific way. As illustrated, the secondtraining image is generated for the user eye while it is gazing a secondgaze point 1250 along the same gaze angle as in the first trainingimage. In this way, the only possible solution is the first solution(e.g., the first angle “a1” 1232 and the first distance “d1” 1234).Another way is to use the same gaze point 1220 for the second trainingimage, but change the distance between the user eye to the camera 1210(e.g., by moving the camera closer or farther from the user eye whilethe user eye maintains its gaze on the gaze point 1220). Yet another wayis to maintain the same user eye-to-camera distance, introduce a secondgaze point (not necessarily at the same gaze angle as in the specificway illustrated in FIG. 12) and generate the second training image whilethe user eye is gazing at that second gaze point. Of course, acombination of these different ways can be used to generate multipletraining images for different user eyes, different gaze angles, anddifferent user eye-to-camera distances. The same camera (or cameraconfiguration for multiple camera) may, but need not, be used togenerate the training images because the training does not depend on aspecific camera or screen configuration.

Once the training images are generated, they are input to the neuralnetwork for the training. In particular, the neural network predicts thegaze angles and the user eye to-camera distances from these trainingimages. The loss function can be defined based relative to gaze anglesand user eye to-camera distances. In the interest of clarity, considerthe first training image described herein above (e.g., generated whenthe user eye was gazing at the gaze point 1220). Based on that trainingimage (and the second training image), the neural network predicts thefirst solution (e.g., the first angle “a1” 1232 and the first distance“d1” 1234). Let us assume that the first angle “a1” 1232 and the firstdistance “d1” 1234 are the actual gaze angle and distance, which areknown in the training (e.g., they are ground truth measurements that canbe stored in a training label associated with the first training image).Let us also assume that the prediction is not completely accurate (e.g.,the predicted angle deviates from the first angle “a1” 1232 and thepredicted distance deviates from the first distance “d1” 1234). The lossfunction includes a distance term and an angle term. The distance termis the difference between the predicted distance and the ground truthdistance (e.g., the actual first distance “d1” 1234). The angle term isthe difference between the predicted gaze angle and the ground truthgaze angle (e.g., the actual first angle “a1” 1232). The goal of thetraining is to update the parameters of the neural network such that itsloss function is minimized, where minimizing the loss function includesminimizing the angle term and the distance term such that the predictedangle is as close as possible to the ground truth angle and thepredicted distance is as close as possible to the ground truth distance.

In an example, this angle term and the distance term can be replacedwith a single distance term. For instance, a predicted gaze line isgenerated at the predicted distance away from the camera and has thepredicted gaze angle. The distance (e.g., the shortest distance) betweenthe gaze point 1220 and the predicted gaze line is measured (this isshown in FIG. 9 as the distance 970). If the prediction was completelyaccurate, this gaze point-to-predicted gaze line distance would be zero.However in other cases, the loss function is minimized (and the neuralnetwork parameters are updated), by minimizing the gazepoint-to-predicted gaze line distance such that the predicted gaze lineis as close to the gaze point 1220 as possible.

In this example also, the loss function can include a penalty term. Morespecifically, when an origin of a predicted gaze line falls outside thecorresponding training image (e.g., referring back to the above example,if “d1” 1234 is large enough such that the eye 1230 would be outside thefirst training image), a penalty is added to the loss function (e.g.,the penalty has a predefined value). Likewise, if a predicted distancecorrection is over a certain threshold in either direction (e.g., fortypercent in either direction), the same or another penalty is added tothe loss function.

FIG. 13 illustrates an example flow for training a neural network,according to an embodiment. FIG. 14 also illustrates an additionalexample flow for the training, where this training uses embeddedcalibration parameters according to an embodiments. A computer system isdescribed as performing the operations of the example flows. In anexample, the computer system performs the training and stores code ofthe neural network. Upon completion of the training, the computer systemmay receive images from eye tracking system and use the neural networkto respond with 3D gaze predictions. Additionally or alternatively, theeye tracking systems may download the code of the neural network fromthe computer system.

Instructions for performing the operations of the illustrative flows canbe stored as computer-readable instructions on a non-transitorycomputer-readable medium of the computer system. As stored, theinstructions represent programmable modules that include code or dataexecutable by a processor(s) of the computer system. The execution ofsuch instructions configures the eye computer system to perform thespecific operations shown in the figures and described herein. Eachprogrammable module in combination with the processor represents a meansfor performing a respective operation(s). While the operations areillustrated in a particular order, it should be understood that noparticular order is necessary and that one or more operations may beomitted, skipped, and/or reordered.

The example flow of FIG. 13 starts at operation 1302, where the computersystem accesses training images that include a first set of trainingimages and a second set of training images. Some or all of the trainingimages in the first set show user eyes associated with gaze points in aplane of a camera. Some or all of the training images in the second setshow user eyes associated with gaze points outside the plane of thecamera. In this way, diversity of training images showing user eyesgazing at gaze points inside and outside the plane is achieved.

At operation 1304, the computer system trains the neural network basedon the training images. Generally, the training includes updatingparameters of the neural network (e.g., weights of connections betweennodes across layers of the neural network) to minimize a loss functionof the neural network. The loss function can use an angle term and/or adistance term as explained herein above in connection with FIG. 12. Inan example, operation 1304 includes two additional operations 1306 and1308.

At operation 1306, the computer system inputs a first training image anda second training image from the training images to the neural network.The first training image belongs to the first set of training images.The second training image belongs to the second set of training images.In a specific example, the first training image shows the user eye whilegazing at a gaze point according to a gaze angle and second trainingimage shows the user eye while gazing at another gaze point according tothe gaze angle. In another specific example, the first training imageand the second training image show the user eye while gazing at the gazepoint in a gaze angle. In this example, the first training imagecorresponds to a first distance between the camera and the user eye, andthe second training image corresponds to a second distance between thecamera and the user eye. In yet another specific example, the firsttraining image shows the user eye while gazing at the gaze pointaccording to a gaze angle and the second training image shows the usereye while gazing at another gaze point according to a different gazeangle. In these examples, the first and second training images can beinput as a pair.

As used herein in connection with the flow of FIG. 13, a training imagerefers to a set of training images that includes a warped image thatshows an eye of a person and, optionally, a warped image of the othereye of the person and a warped image of the face of the person. In otherwords, the images used in the training are of similar types that theimages that would be used upon completion of the training (e.g., asillustrated in connection with FIG. 9) and can be generated followingsimilar operations of projecting, rotating, scaling, and cropping.

At operation 1308, the computer system minimizes the loss function ofthe neural network based on a distance between a gaze point and a gazeline. The gaze point is associated with one of the first training imageor the second training image. The gaze line is predicted by the neuralnetwork for a user eye from the one of the first training image or thesecond training image. Operations 1306-1308 are repeated across many ifnot all of the training images to complete the training of the neuralnetwork.

Turning to FIG. 14, the training of the neural network can also involvecalibration parameters, such that when the neural network predicts gazeinformation (as described herein above), the prediction is based on suchparameters. A designer of the neural network and/or an eye trackingsystem need not specify the calibration parameters. Instead, thecalibration parameters are embedded and it is sufficient for thedesigner to specify the number of these parameters. During the training,the calibration parameters are initialized and then updated along withthe parameters of the neural network (e.g., the weights between theconnections in the network) based on minimizing the loss function of theneural network. In this way, the parameters of the neural network areupdated during the training based on also the calibration parameters.During the training of the neural network, some of the operations of theflow of FIG. 14 can be implemented and used in conjunction with theoperations of FIG. 13. Upon completion of the training, remainingoperations of the flow of FIG. 14 can be implemented and used inconjunction with the operations of FIG. 11. In this case, thecalibration parameters of the user are set based on minimizing the lossfunction given a set of calibration images. These calibration parametersare then input to the neural network in support of the 3D gazeprediction.

Although FIG. 14 describes learning the calibration parameters withinthe context of a neural network that predicts gaze information, theembodiments are not limited as such. Generally, the operations of FIG.14 similarly apply in connection with a neural network that isassociated with a system and that is trained for a task (e.g.,predicting a particular outcome), where proper operation of the systeminvolves calibration. Furthermore, in the interest of clarity ofexplanation, the example flow of FIG. 14 is described in connection witha training image, calibration image, and image. However, the exampleflow similarly apply for multiple training images, multiple calibrationimages, and multiple images.

As illustrated, the example flow of FIG. 14 starts at operation 1402,where the computer system accesses a training image associated with aperson. The training image shows a face and/or an eye(s) of the person.Generally, the person has an index “i” and the training image can alsobe indexed with index “i” (e.g., the label or metadata data of thetraining image includes the index “i”). The training image may beavailable from a data store and accessed therefrom as part of thetraining process. The number “n” is an integer number greater than zerothat a designer of the neural network specifies. In an example, thenumber “n” is between two and ten. In a specific example, the number “n”is three.

At operation 1404, the computer system initializes “n” calibrationparameters for the person. For instance, the values of these calibrationparameters are set to zero or some other first value.

At operation 1406, the computer system inputs the training image and the“n” calibration parameters to the neural network. For example, thetraining image is input to the relevant Res18 CNN of the neural network,while the “n” calibration parameters are input to concatenation layersassociated with fully connected modules of the neural network wherethese modules are responsible for predicting 2D gaze origins and 2D gazedirections.

At operation 1408, the computer system updates the “n” calibrationparameters and parameters of the neural network (referred to herein asnetwork parameters and include, for example, weights of connectionsbetween nodes of the neural network) based on minimizing a loss functionof the neural network. The loss function is minimized based on thetraining image and the “n” calibration parameters thoughbackpropagation. Accordingly, the first values (e.g., zeros) of the “n”calibration parameters are updated to second values.

Operations 1402-1408 may be implemented as part of training the neuralnetwork by the computer system. Upon completion of the training, theneural network may be available for use by a user of an eye trackingsystem. When using the eye tracking system, this system may becalibrating by calibrating the neural network specifically to the user.

At operation 1410, the computer system initializes the “n” calibrationparameters for the user. This operation is similar to operation 1404. Inan example, the initialization may, but need not be, to the same firstvalues (e.g., zeros). In another example, the initialization is to theupdated values (e.g., the second values) of the “n” calibrationparameters as determined under operation 1408.

At operation 1412, the computer system generates a calibration image forthe user. For example, the computer system instructs the user to gaze ata known gaze point and generates the calibration image accordingly. Thecalibration image shows an eye (or two eyes, or a face as application)of the user based on image data generated by a camera associated withthe eye tracking system.

At operation 1414, the computer system inputs the calibration image andthe calibration parameters to the neural network. This operation issimilar to operation 1406.

At operation 1416, the computer system updates the “n” calibrationparameters for the user without updating the network parameters thatwere set during the training. The “n” calibration parameters are updatedby at least minimizing the loss function based on the calibration imageand the “n” calibration parameters, where the minimization does notchange the network parameters. Here, the calibration image has a knowncalibration point. The known calibration point is used a ground truthfor user gaze in the minimization of the loss function. The computersystem iteratively updates the “n” calibration parameters whilemaintaining the network parameters through backpropagation until theloss function is minimized. Accordingly, the initialized values (e.g.,the first values) of the “n” calibration parameters as performed underoperation 1410 are updated to third values. Typically, the third valuesare different from the second values of the “n” calibration parametersas determined under operation 1408.

Operations 1410-1416 may be implemented as part of calibrating theneural network by the computer system. Upon completion of thecalibration, the neural network may be available for generating 3D gazeinformation for the user.

At operation 1418, the computer system inputs an image and the “n”calibration parameters as updated for the user to the neural network.The image shows the eye (the two eyes, or the face as applicable) of theuser based on additional image data generated by the camera. Thisoperation is similar to operation 1406.

At operation 1420, the computer system receives a prediction from theneural network based on the image and the “n” calibration parameters.The prediction includes a distance correction, a 2D gaze origin of theeye of the user in the image, and a 2D gaze direction of the eye of theuser in the image.

The disclosure has now been described in detail for the purposes ofclarity and understanding. However, it will be appreciated that certainchanges and modifications may be practiced within the scope of theappended claims.

The above description provides exemplary embodiments only, and is notintended to limit the scope, applicability or configuration of thedisclosure. Rather, the above description of the exemplary embodimentswill provide those skilled in the art with an enabling description forimplementing one or more exemplary embodiments. It being understood thatvarious changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the disclosure as setforth herein.

For example, any detail discussed with regard to one embodiment may ormay not be present in all contemplated versions of that embodiment.Likewise, any detail discussed with regard to one embodiment may or maynot be present in all contemplated versions of other embodimentsdiscussed herein. Finally, the absence of discussion of any detail withregard to embodiment herein shall be an implicit recognition that suchdetail may or may not be present in any version of any embodimentdiscussed herein.

Specific details are given in the following description to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits,systems, networks, processes, and other elements in the disclosure maybe shown as components in block diagram form in order not to obscure theembodiments in unnecessary detail. In other instances, well-knowncircuits, processes, algorithms, structures, and techniques may be shownwithout unnecessary detail in order to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process may beterminated when its operations are completed, but could have additionalsteps not discussed or included in a figure. Furthermore, not alloperations in any particularly described process may occur in allembodiments. A process may correspond to a method, a function, aprocedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination corresponds to a return of the functionto the calling function or the main function.

The term “machine-readable medium” includes, but is not limited totransitory and non-transitory, portable or fixed storage devices,optical storage devices, wireless channels and various other mediumscapable of storing, containing or carrying instruction(s) and/or data. Acode segment or machine-executable instructions may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

Furthermore, embodiments of the disclosure may be implemented, at leastin part, either manually or automatically. Manual or automaticimplementations may be executed, or at least assisted, through the useof machines, hardware, software, firmware, middleware, microcode,hardware description languages, or any combination thereof. Whenimplemented in software, firmware, middleware or microcode, the programcode or code segments to perform the necessary tasks may be stored in amachine readable medium. A processor or processors may perform thenecessary tasks.

As used herein, the phrase “a first thing based on a second thing,” andthe like, may mean that the first thing is based solely on the secondthing, or that the first thing is based on the second thing as well asone or more additional things.

What is claimed is:
 1. A computer-implemented method comprising:training, by a computer system, a neural network by at least: inputtinga training image and a first calibration parameter to the neuralnetwork, the training image showing an eye of a person, and updating thefirst calibration parameter and a network parameter of the neuralnetwork based on minimizing a loss function of the neural network,wherein the loss function is minimized based on the training image andthe first calibration parameter; upon completion of the training,calibrating by the computer system the neural network for a user by atleast: inputting a calibration image and a second calibration parameterto the neural network, the calibration image showing an eye of the userbased on image data generated by a camera associated with an eyetracking system of the user, and updating the second calibrationparameter without updating the network parameter of the neural network,wherein the second calibration parameter is updated by at leastminimizing the loss function based on the calibration image and thesecond calibration parameter; and upon completion of the calibrating,generating by the computer system three dimensional (3D) gazeinformation for the user by at least: inputting an image and the secondcalibration parameter to the neural network, the image showing the eyeof the user based on additional image data generated by the camera, andreceiving a prediction from the neural network based on the image andthe second calibration parameter, wherein the prediction comprises adistance correction, a two dimensional (2D) gaze origin of the eye ofthe user in the image, and a 2D gaze direction of the eye of the user inthe image.
 2. The computer-implemented method of claim 1, furthercomprising: generating, by the computer system, a corrected distancebetween the eye of the user and the camera by at least updating anestimated distance between the eye of the user and the camera based onthe distance correction, and wherein the 3D gaze information for the eyeof the user is generated based on the 2D gaze origin, the 2D gazedirection, and the corrected distance.
 3. The computer-implementedmethod of claim 2, wherein the image is a 2D image, and furthercomprising: determining, by the computer system, the estimated distancebetween the camera and the eye of the user based on the 2D image; andestimating, by the computer system, a position of the eye of the user ina 3D space based on the corrected distance and on a position of thecamera in the 3D space, wherein the 3D gaze information comprises theposition of the eye in the 3D space.
 4. The computer-implemented methodof claim 3, further comprising: estimating, by the computer system, a 3Dgaze direction from the position of the eye in the 3D space based on the2D gaze origin and the 2D gaze direction, wherein the 3D gazeinformation comprises the 3D gaze direction.
 5. The computer-implementedmethod of claim 2, wherein inputting the first calibration parametercomprises initializing the first calibration parameter to a first value,wherein updating the first calibration parameter comprises updating thefirst value to a second value.
 6. The computer-implemented method ofclaim 5, wherein inputting the second calibration parameter comprisesinitializing the second calibration parameter to the first value,wherein updating the first calibration parameter comprises updating thefirst value to a third value.
 7. The computer-implemented method ofclaim 5, wherein each of the training and the calibrating uses “n”calibration parameters that include the first calibration parameter andthe second calibration parameter, and wherein “n” is an integer in therange of two to ten.
 8. The computer-implemented method of claim 7,wherein “n” is equal to three.
 9. A computer system comprising: aprocessor; and a memory storing computer-readable instructions that,upon execution by the processor, configure the computer system toperform operations comprising: training a neural network by at least:inputting a training image and a first calibration parameter to theneural network, the training image showing an eye of a person, andupdating the first calibration parameter and a network parameter of theneural network based on minimizing a loss function of the neuralnetwork, wherein the loss function is minimized based on the trainingimage and the first calibration parameter; and upon completion of thetraining, calibrating by the computer system the neural network for auser by at least: inputting a calibration image and a second calibrationparameter to the neural network, the calibration image showing an eye ofthe user based on image data generated by a camera associated with aneye tracking system of the user, and updating the second calibrationparameter without updating the network parameter of the neural network,wherein the second calibration parameter is updated by at leastminimizing the loss function based on the calibration image and thesecond calibration parameter.
 10. The computer system of claim 9,wherein the operations further comprise: upon completion of thecalibrating, generating by the computer system three dimensional (3D)gaze information for the user by at least: inputting an image and thesecond calibration parameter to the neural network, the image showingthe eye of the user based on additional image data generated by thecamera, and receiving a prediction from the neural network based on theimage and the second calibration parameter, wherein the predictioncomprises a distance correction, a two dimensional (2D) gaze origin ofthe eye of the user in the image, and a 2D gaze direction of the eye ofthe user in the image.
 11. The computer system of claim 9, whereintraining the neural network further comprises: inputting a secondtraining image to the neural network, wherein the training image showsthe eye of the person while gazing at a gaze point according to a gazeangle, wherein the second training image shows the eye of the personwhile gazing at another gaze point according to the gaze angle.
 12. Thecomputer system of claim 9, wherein training the neural network furthercomprises: inputting a second training image to the neural network,wherein the training image and the second training image show the eye ofthe person while gazing at a gaze point in a gaze angle, wherein thetraining image corresponds to a first distance between a first cameraand the eye of the person, and wherein the second training imagecorresponds to a second distance between the first camera and the eye ofthe person.
 13. The computer system of claim 9, wherein training theneural network further comprises: inputting a second training image tothe neural network, wherein the training image shows the eye of theperson while gazing at a gaze point according to a gaze angle, whereinthe second training image shows the eye of the person while gazing atanother gaze point according to a different gaze angle.
 14. The computersystem of claim 9, wherein training the neural network furthercomprises: inputting a second training image to the neural network,wherein the loss function of the neural network is minimized furtherbased on a distance between a gaze point and a gaze line, wherein thegaze point is associated with one of the training image or the secondtraining image, and wherein the gaze line is predicted by the neuralnetwork for eye of the person shown in the training image.
 15. Anon-transitory computer-readable medium storing instructions that, uponexecution on a computer system, configure the computer system to performoperations comprising: training a neural network by at least: inputtinga training image and a first calibration parameter to the neuralnetwork, the training image showing an eye of a person, and updating thefirst calibration parameter and a network parameter of the neuralnetwork based on minimizing a loss function of the neural network,wherein the loss function is minimized based on the training image andthe first calibration parameter; and upon completion of the training,calibrating by the computer system the neural network for a user by atleast: inputting a calibration image and a second calibration parameterto the neural network, the calibration image showing an eye of the userbased on image data generated by a camera associated with an eyetracking system of the user, and updating the second calibrationparameter without updating the network parameter of the neural network,wherein the second calibration parameter is updated by at leastminimizing the loss function based on the calibration image and thesecond calibration parameter.
 16. The non-transitory computer-readablemedium of claim 15, wherein the operations further comprise: uponcompletion of the calibrating, generating by the computer system threedimensional (3D) gaze information for the user by at least: inputting animage and the second calibration parameter to the neural network, theimage showing the eye of the user based on additional image datagenerated by the camera, and receiving a prediction from the neuralnetwork based on the image and the second calibration parameter, whereinthe prediction comprises a distance correction, a two dimensional (2D)gaze origin of the eye of the user in the image, and a 2D gaze directionof the eye of the user in the image.
 17. The non-transitorycomputer-readable medium of claim 15, wherein each of the training andthe calibrating uses “n” calibration parameters that include the firstcalibration parameter and the second calibration parameter, and wherein“n” is an integer in the range of two to ten.
 18. The non-transitorycomputer-readable medium of claim 15, wherein inputting the firstcalibration parameter comprises initializing the first calibrationparameter to a first value, wherein updating the first calibrationparameter comprises updating the first value to a second value.
 19. Thenon-transitory computer-readable medium of claim 18, wherein inputtingthe second calibration parameter comprises initializing the secondcalibration parameter to the first value, wherein updating the firstcalibration parameter comprises updating the first value to a thirdvalue different from the second value.
 20. The non-transitorycomputer-readable medium of claim 18, wherein inputting the secondcalibration parameter comprises initializing the second calibrationparameter to a third value, wherein updating the first calibrationparameter comprises updating the third value to a fourth value.